Adaptive Flocking of a Swarm of Robots Based on Local Interactions

Abstract

This paper presents a novel flocking strategy for a large-scale swarm of robots that enables the robots to navigate autonomously in an environment populated with obstacles. Robot swarms are often required to move toward a goal while adapting to changes in environmental conditions in many applications. Based on the observation of the swimming behavior of a school of tunas, we apply their unique patterns of behavior to the autonomous adaptation of the shape of robot swarms. Specifically, each robot dynamically selects two neighboring robots within its sensing range and maintains a uniform distance with them. This enables three neighboring robots to form a regular triangle and remain stable in the presence of obstacles. Therefore, the swarm can be split into multiple groups or re-united into one according to environmental conditions. More specifically, assuming that robots are not allowed to have individual identification numbers, a pre-determined leader, memories of previous perceptions and actions, and direct communications to each other, we verify the validity of the proposed algorithm using the in-house simulator. The results show that a swarm of robots repeats the process of partition and maintenance passing through multiple narrow passageways